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Vivek Kulkarni

Researcher at Stanford University

Publications -  48
Citations -  4379

Vivek Kulkarni is an academic researcher from Stanford University. The author has contributed to research in topics: Language model & Word usage. The author has an hindex of 19, co-authored 45 publications receiving 3727 citations. Previous affiliations of Vivek Kulkarni include Stony Brook University & University of California, Santa Barbara.

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Theano: A Python framework for fast computation of mathematical expressions

Rami Al-Rfou, +111 more
TL;DR: The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.
Journal ArticleDOI

The Diversity-Innovation Paradox in Science

TL;DR: This paper used text analysis and machine learning to answer a series of questions: How do we detect scientific innovations? Are underrepresented groups more likely to generate scientific innovations, and are the innovations of under-represented groups adopted and rewarded?
Proceedings ArticleDOI

Statistically Significant Detection of Linguistic Change

TL;DR: This paper proposed a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words on the Internet, where rapid exchange of ideas can quickly change a word's meaning.
Proceedings ArticleDOI

Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings

TL;DR: WalkLeTS as mentioned in this paper generates multiscale representations by sub-sampling short random walks on the vertices of a graph, which can be used to learn a series of latent representations, each of which captures successively higher order relationships from the adjacency matrix.
Proceedings Article

POLYGLOT-NER: Massive Multilingual Named Entity Recognition

TL;DR: This article proposed a system that builds massive multilingual annotators with minimal human expertise and intervention using Wikipedia and Freebase data sets, which does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules.